A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas
| dc.contributor.author | Ding, Zhiyue | |
| dc.contributor.author | Matthews, Lorin | |
| dc.contributor.author | Hyde, Truell | |
| dc.date.accessioned | 2022-03-21T19:33:50Z | |
| dc.date.available | 2022-03-21T19:33:50Z | |
| dc.date.issued | 2021-06 | |
| dc.description.abstract | Nonlinear frequency response analysis is a widely used method for determining system dynamics in the presence of nonlinearities. In dusty plasmas, the plasma–grain interaction (e.g. grain charging fluctuations) can be characterized by a single-particle non-linear response analysis, while grain–grain non-linear interactions can be determined by a multi-particle non-linear response analysis. Here a machine learning-based method to determine the equation of motion in the non-linear response analysis for dust particles in plasmas is presented. Searching the parameter space in a Bayesian manner allows an efficient optimization of the parameters needed to match simulated non-linear response curves to experimentally measured non-linear response curves. | en_US |
| dc.identifier.citation | Machine Learning Science and Technology, 2, 035017, 2021 | en_US |
| dc.identifier.doi | 10.1088/2632-2153/abe7b7 | |
| dc.identifier.uri | https://hdl.handle.net/2104/11776 | |
| dc.language.iso | en | en |
| dc.publisher | IOP PUblishing | en_US |
| dc.title | A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas | en_US |
| dc.type | Article | en |
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